Regional unmanned aerial vehicle safety risk assessment method

Abstract

The invention discloses a regional unmanned aerial vehicle safety risk assessment method, which comprises the following steps: acquiring parameters such as unmanned aerial vehicle body length, wingspan width and body height, and preset task scene data such as regional tall building area ratio, unmanned aerial vehicle density, specific parameters and quantity of aircrafts; based on the data, regional ground and airspace safety risk values are calculated. And determining visibility, rainfall and wind speed as meteorological risk assessment indexes, performing weighting by using an analytic hierarchy process, and calculating a meteorological risk assessment coefficient. And constructing a regional unmanned aerial vehicle safety risk assessment model by integrating assessment data obtained under three environmental conditions of weather, ground and airspace. And comprehensively evaluating the safety risk of the unmanned aerial vehicle in the region by using the model. According to the technical scheme of the invention, three environmental factors of meteorological conditions, ground conditions and airspace conditions are integrated, and various environmental risk types possibly experienced by the unmanned aerial vehicle in the operation process can be comprehensively evaluated.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle safety risk assessment, and in particular relates to a regional unmanned aerial vehicle safety risk assessment method. Background Art

[0002] Research on safety risk assessment of drones generally focuses on the impact of drone system equipment platforms and external conditions. At present, the research on safety assessment of drone equipment level has been relatively complete. In the research, the structure of drones is refined, including subsystems such as flight control systems, communication systems, power systems, hydraulic systems, etc., and they are split into parts. According to the independence and differences of each component structure, the impact of its own failure on the safety of drones is studied. However, in recent years, more and more studies have turned their attention to the impact of low-altitude geographical environment on drones. At present, when analyzing the meteorological threats faced by drones, it is generally a qualitative study, lacking quantitative evaluation criteria and model support. When studying collisions, the Reich collision risk model is mainly established for the collision risk of parallel routes, studying the safety risks of the longitudinal, lateral and vertical dimensions of the aircraft, and analyzing the collision risk caused by the relative position of the aircraft. Compared with the Reich model, the Event model is more inclusive in practical applications, has significant application advantages in routes with complex influencing factors, and has more objective data. However, under the premise of considering the comprehensive distribution of the regional environment, there is a lack of evaluation methods for the instantaneous safety risks faced by drones when they are in a certain state in the region. Summary of the invention

[0003] The purpose of the present invention is to provide a regional drone safety risk assessment method to solve the problems existing in the above-mentioned prior art.

[0004] To achieve the above object, the present invention provides a regional drone safety risk assessment method, comprising:

[0005] Obtaining specific parameters of the drone and preset mission scenario information data, wherein the specific parameters of the drone include body length, wingspan width and body height, and the preset mission scenario information data includes area ratio data of tall buildings in the area, drone density data in the area, specific parameters of aircraft in the area, and data on the number of aircraft in the area;

[0006] Calculate the regional ground safety risk value and airspace safety risk value based on the specific parameters of the drone and the preset mission scenario information data;

[0007] Determine meteorological risk assessment indicators, the meteorological risk assessment indicators include visibility, rainfall and wind speed; weight each of the meteorological risk assessment indicators based on the analytic hierarchy process, and calculate the meteorological risk assessment coefficient based on each of the meteorological risk assessment indicators and the corresponding weight;

[0008] A regional UAV safety risk assessment model is constructed based on the meteorological risk assessment coefficient, the ground safety risk value and the airspace safety risk value, and a safety risk assessment of UAVs in the region is performed based on the regional UAV safety risk assessment model.

[0009] Optionally, the calculation process of the regional ground safety risk value specifically includes:

[0010] The contact area and relative speed between the drone and the building are calculated based on the specific parameters of the drone and the area ratio data of tall buildings in the area; and the ground safety risk value is calculated based on the contact area, relative speed and drone density data in the area.

[0011] Optionally, the ground safety risk value is calculated based on the contact area, relative speed and drone density data in the area, and the specific calculation formula is:

[0012] A=b1h1 cosα

[0013] R G =n B ·ρAv1

[0014] Where A is the contact area, α is the angle between the plane formed by the wingspan and height directions of the drone and the contact plane, b1 and h1 are the wingspan and height of the drone, v1 is the relative speed of the drone to the building in the vertical direction of the section, and R is G represents the security risk brought by ground buildings in the area per unit time, ρ is the density of drones in the area, and n B This is the area ratio of tall buildings in the region.

[0015] Optionally, the calculation process of the airspace safety risk value specifically includes:

[0016] The relative speed between the drone and the aircraft is calculated based on the specific parameters of the drone and the parameter data of other aircraft in the area; the airspace safety risk value is calculated based on the relative speed and the drone density and aircraft quantity data in the area.

[0017] Optionally, the ground safety risk assessment coefficient is calculated based on the relative speed and the drone density data in the area, and the specific calculation formula is:

[0018] V=(a+v2t)bh

[0019]

[0020] Where V is the volume swept in time t, v2 is the relative speed between the UAV and other aircraft in the airspace, a, b, h are the particle distances between the UAV and the aircraft in the direction of aircraft movement, wingspan direction and vertical direction respectively; R A is the safety risk of drones in the airspace of the region, N is the number of such aircraft in the region, and ρ is the density of drones in the region.

[0021] Optionally, the calculation process of the meteorological risk assessment coefficient specifically includes:

[0022] Determine meteorological risk assessment indicators, determine the grade assessment factors corresponding to each meteorological risk assessment indicator based on the severity of the weather, weight each meteorological risk assessment indicator based on the hierarchical analysis method, and calculate the meteorological risk assessment coefficient based on the weights and grade assessment factors corresponding to each meteorological risk assessment indicator.

[0023] Optionally, the meteorological risk assessment coefficient is calculated based on the weights and grade assessment factors corresponding to each of the meteorological risk assessment indicators, and the specific calculation formula is:

[0024] η=w1×η F +w2×η R +w3×η W

[0025] Where η is the meteorological risk condition coefficient of the UAV, η F , η R , η w are the evaluation factors corresponding to fog, rainfall and wind phenomena respectively, and w1, w2 and w3 are the weights corresponding to fog, rainfall and wind phenomena respectively.

[0026] Optionally, the regional drone safety risk assessment model is specifically:

[0027]

[0028] In the formula, R U is the safety risk of UAV, η is the meteorological risk assessment coefficient of UAV, R G Represents the safety risk brought by the ground buildings in the area, R A It is a risk to airspace security in the region.

[0029] The technical effects of the present invention are:

[0030] As a regional UAV safety risk assessment method, the present invention integrates three types of environmental factors: meteorological conditions, ground conditions, and airspace conditions, constructs a regional UAV safety risk assessment model, and proposes a regional UAV safety risk assessment method that considers environmental impacts. It can comprehensively assess various types of environmental risks that UAVs may experience during operation, provide a usability reference for improving UAV safety risks, and provide a possible method support for better improving UAV missions and services. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0033] Figure 1 A schematic diagram of a safety risk accident occurring during the flight of a UAV in an embodiment of the present invention;

[0034] Figure 2 is an index evaluation system in an embodiment of the present invention;

[0035] Figure 3 A top view of a region in an embodiment of the present invention;

[0036] Figure 4 is a schematic diagram of the relative speed between the UAV and the building in an embodiment of the present invention;

[0037] Figure 5 Schematic diagram of the center distance between the UAV and the aircraft in the three directions of the aircraft movement direction, wingspan direction and vertical direction in the embodiment of the present invention;

[0038] Figure 6 This is an evaluation flow chart in an embodiment of the present invention. DETAILED DESCRIPTION

[0039] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as limiting the present invention, but should be understood as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0040] It should be understood that the terms described in the present invention are only for describing special embodiments and are not intended to limit the present invention. In addition, for the numerical range in the present invention, it should be understood that each intermediate value between the upper and lower limits of the scope is also specifically disclosed. Each smaller range between the intermediate value in any stated value or stated range and any other stated value or intermediate value in the described range is also included in the present invention. The upper and lower limits of these smaller ranges can be independently included or excluded in the scope.

[0041] Unless otherwise indicated, all technical and scientific terms used herein have the same meanings as those of ordinary skill in the art generally understood by the present invention. Although the present invention describes only preferred methods, any method similar or equivalent to that described herein may also be used in the implementation or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods related to the documents. In the event of a conflict with any incorporated document, the content of this specification shall prevail.

[0042] It will be apparent to those skilled in the art that various modifications and variations may be made to the specific embodiments of the present invention description without departing from the scope or spirit of the present invention. Other embodiments derived from the present invention description will be apparent to those skilled in the art. The present application description and examples are exemplary only.

[0043] The words “include,” “including,” “have,” “contain,” etc. used in this article are open-ended terms, meaning including but not limited to.

[0044] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0045] like Figure 1 - Figure 6As shown, in this embodiment, a regional UAV safety risk assessment method is provided, including: obtaining specific parameters of the UAV and preset mission scenario information data, the specific parameters of the UAV include body length, wingspan width and body height, and the preset mission scenario information data include regional tall building area ratio data, regional UAV density data, regional aircraft specific parameters and regional aircraft quantity data; calculating regional ground safety risk value and airspace safety risk value based on the specific parameters of the UAV and the preset mission scenario information data; determining meteorological risk assessment indicators, the meteorological risk assessment indicators include visibility, rainfall and wind speed; weighting each of the meteorological risk assessment indicators based on the hierarchical analysis method, and calculating the meteorological risk assessment coefficient based on each of the meteorological risk assessment indicators and the corresponding weight; constructing a regional UAV safety risk assessment model based on the meteorological risk assessment coefficient, the regional ground safety risk value and the airspace safety risk value, and performing safety risk assessment of UAVs in the region based on the regional UAV safety risk assessment model.

[0046] The specific steps of this embodiment include:

[0047] When performing their missions, drones are completely exposed to external natural conditions and generally have to go through a long mission time. Adverse meteorological conditions will have an impact on the safety risk of drones, and the degree of impact is expressed by the meteorological risk condition coefficient. Through the analysis and research on the meteorological risk of drones, fog, rain, and wind are determined as the influencing factors of the meteorological risk of drones, and visibility, rainfall, and wind speed are used as influencing indicators of the meteorological risk assessment of drones. The risk level is determined according to the indicator type and the severity of the weather. There are four levels in total, and each level corresponds to a fixed risk assessment factor value between 0-1. The higher the risk level, the higher the assessment factor value. The assessment factors corresponding to fog, rain, and wind phenomena are η, respectively. F , η R , η w express.

[0048] Due to the differences in the way the three types of weather affect the operation of drones, they have different degrees of impact on the safety threat of drone flights. It is necessary to select an appropriate method to determine the role of the three types of weather on drone safety in different situations, and use the hierarchical analysis method to determine the weight value of each type. By comparing the three elements of fog, rainfall and wind in pairs, a judgment matrix is ​​constructed. The form of the judgment matrix is ​​as follows:

[0049]

[0050] The judgment matrix has the following properties:

[0051] Calculate the geometric mean of each row of the judgment matrix A, and normalize it, that is, w1+w2+w3=1, to get the approximate value of the required eigenvector W=(w1,w2,w3) T , which is also the relative weight of each element.

[0052] By formula The maximum eigenvalue of the judgment matrix is ​​obtained, and a consistency check is performed on the judgment matrix. If the consistency check is passed, the weight is valid.

[0053] The UAV meteorological risk condition coefficient is determined as η, and the calculation formula is as follows:

[0054] η=w1×η F +w2×η R +w3×η W

[0055] The drone will be affected by ground buildings during flight. The height of some buildings will exceed the flight altitude of the drone, requiring the drone to flexibly avoid obstacles during operation. Therefore, a drone ground collision risk model is established to evaluate the ground safety risk of drones.

[0056] Assume that the average speed of the UAV is v (km / h), and the length, wingspan and height (m) of the UAV are a1, b1, h1 respectively.

[0057]

[0058] Assume that the area ratio of tall buildings in the region is n B , S represents the total horizontal area of ​​the mission airspace, S B Represents the floor space of buildings above the drone's flight altitude.

[0059] Since the actual size of the transport drone is very different from that of the building, the curvature of the contact surface between the obstacle and the drone is ignored, and the contact area when the two collide is calculated as a plane. The actual flight speed of the drone is perpendicular to the wingspan and height directions of the drone. The contact area A is the projection area of ​​the drone on the approximate contact plane. α is the angle between the plane formed by the wingspan and height directions of the drone and the contact plane.

[0060] A=b1h1 cosα

[0061] The relative speed of the UAV to the building in the direction perpendicular to the section is the velocity component of the UAV's actual speed in the direction perpendicular to the contact section, and the value of this component is v1.

[0062] Security risks brought by ground buildings per unit time in the area:

[0063] RG =n B ·ρAv1

[0064] Where ρ is the density of drones in the area (units / km 3 ).

[0065] The conditions of the airspace itself are affected by other aircraft in the airspace, which will have a certain impact on the flight of drones and may increase regional safety risks. In order to quantitatively evaluate the collision risk brought by aircraft in the airspace to drones during transportation tasks, a drone airspace safety risk assessment model is established.

[0066] Let a1, b1, h1, a2, b2, and h2 be the length, wingspan, and height (m) of the drone and the aircraft, respectively. When a drone collides with an aircraft in the airspace, the distance between the two particles in the direction of the aircraft's movement, the wingspan, and the vertical direction is a, b, and h, respectively. Consider the drone as a particle, assume that the drone is in a relatively static state, the speed of the aircraft relative to the drone is v2, and the volume swept by the aircraft in time t is V = (a + v2t) bh. If the drone is in the swept space, it means that the drone has collided with the aircraft.

[0067] Assume that the number of such aircraft in the region is N, and ρ is the density of drones in the region (units / km 3 ), then the safety risk of drones in the airspace within the region is

[0068] Based on the various environmental risks that drones may experience during operation, the values ​​of various factors, the relationship between indicators and the calculation method within the regional scope are used to finally obtain the drone safety risk R. U :

[0069]

[0070] This embodiment, as a regional drone safety risk assessment method that affects the environment, integrates three types of environmental factors: meteorological conditions, ground conditions, and airspace conditions, constructs a regional drone safety risk assessment model, and proposes a regional drone safety risk assessment method that considers environmental impacts. It can comprehensively assess various types of environmental risks that drones may experience during operation, provide a usability reference for improving drone safety risks, and provide a possible method support for better improving drone missions and services.

[0071] Specific examples of this embodiment include:

[0072] UAVs will be affected by the environment during flight, and there is a certain probability of safety risk accidents after being threatened by the environment, such as Figure 1As shown, resulting in loss of product and mission.

[0073] In order to establish a UAV safety risk assessment method that considers environmental impact, it is necessary to find out the environmental factors that affect UAV flight and determine the indicator evaluation system based on the actual situation of environmental factors, such as Figure 2 As shown, various environmental influencing factors are evaluated and processed, and mathematical and physical model calculations are performed based on the analysis to obtain an evaluation method for each factor, thereby comprehensively establishing a drone safety risk assessment model.

[0074] The assessment method is divided into three parts: meteorological risk, ground condition risk and airspace risk. Bad meteorological conditions will increase the probability of UAVs colliding with obstacles on the ground and in the airspace, which is not conducive to flexible obstacle avoidance. Therefore, the meteorological risk is represented by the meteorological condition coefficient. In terms of the total risk, it is a multiplication relationship with the collisions directly caused by the ground and airspace, which affects the possibility of the safety risk of collision.

[0075] The risk level is determined according to the indicator type and the severity of the weather, which is divided into four levels, I-IV. Each level corresponds to a fixed risk assessment factor value, which is 0.2, 0.4, 0.7 and 0.9 respectively. The higher the risk level, the higher the assessment factor value. The assessment factors corresponding to fog, rainfall and strong wind are η F , η R , η w express.

[0076] Due to the differences in the way the three types of weather affect the operation of drones, they have different degrees of impact on the safety threat of drone flights. It is necessary to select an appropriate method to determine the role of the three types of weather on drone safety in different situations, and use the hierarchical analysis method to determine the weight value of each type. By comparing the three elements of fog, rainfall and wind in pairs, a judgment matrix is ​​constructed. The form of the judgment matrix is ​​as follows:

[0077]

[0078] W=(0.3090,0.5816,0.1094) T , which is also the relative weight of each element. If the judgment matrix is ​​checked for consistency, the weight is valid.

[0079] The UAV meteorological risk condition coefficient is determined as η, and the calculation formula is as follows:

[0080] η=0.3090η F +0.5816η R +0.1094η W

[0081] Drones will be affected by ground buildings during flight. Drones that perform tasks such as logistics and transportation generally fly at an altitude of about 50-70 meters. Therefore, the height of some buildings will exceed the level flight altitude of the drone, requiring the drone to flexibly avoid obstacles during operation.

[0082] For the UAV, let the average speed of the UAV be v (km / h). Let the length, wingspan and height (m) of the UAV be a1, b1, h1 respectively.

[0083]

[0084] Assume that the area ratio of tall buildings in the region is n B , S represents the total horizontal area of ​​the mission airspace B Indicates the area of ​​buildings above the drone's flight altitude. Figure 3 As shown, the solid line represents the building outline that exceeds the UAV's flight altitude, and the dotted line represents the building outline that does not exceed the UAV's flight altitude.

[0085] Since the actual size of a small drone is very different from that of a building, the curvature of the contact surface between the obstacle and the drone is ignored, and the contact area when the two collide is calculated as a plane. The actual flight speed of the drone is perpendicular to the wingspan and height directions of the drone. The contact area A is the projection area of ​​the drone on the approximate contact plane. α is the angle between the plane formed by the wingspan and height directions of the drone and the contact plane.

[0086] A=b1h1 cosα

[0087] The relative speed between the drone and the building is the velocity component of the drone’s actual speed in the direction perpendicular to the contact section, and the value of this component is v1. The top view of the drone is as follows: Figure 4 shown.

[0088] Security risks brought by ground buildings per unit time in the area:

[0089] R G =n B ·ρAv1

[0090] Where ρ is the density of drones in the area (units / km 3 ).

[0091] The conditions of the airspace itself are affected by other aircraft in the airspace, which will have a certain impact on the flight of drones and may increase regional safety risks. In order to quantitatively evaluate the collision risk caused by other aircraft in the airspace during the mission, a drone airspace safety risk assessment model is established.

[0092] Assume a1, b1, h1, a2, b2, and h2 are the length, wingspan, and height (m) of the drone and the aircraft respectively. When a drone collides with an aircraft in the airspace, the center distance between the two in the direction of the aircraft's movement, the wingspan direction, and the vertical direction is a, b, and h respectively. Figure 5 As shown. Consider the drone as a point mass, assume that the drone is in a relatively static state, the speed of the aircraft relative to the drone is v2, and the volume swept by the collision body established by a, b, and h in time t is V = (a + v2t) bh. If the drone is located in the swept space, it means that the drone and the aircraft have collided.

[0093] Assume that the number of such aircraft in the region is N, and ρ is the density of drones in the region (units / km 3 ), the safety risk of drones in the airspace within the region is

[0094] According to the UAV mission environment scenario, relevant indicator data is obtained to obtain the meteorological risk condition coefficient η and the ground condition safety risk R in the area. G and airspace safety risks R A Combining the three types of environmental risks, the final drone safety risk R U :

[0095]

[0096] This embodiment is a regional drone safety risk assessment method. It is highly feasible by constructing a drone environmental impact index system for analysis. When determining the drone meteorological risk condition coefficient, the meteorological data such as rainfall and wind speed used can be obtained through detection, and the data source is reliable. The regional drone safety risk is measured by the instantaneous collision risk. Considering the severe weather conditions encountered by the drone, it will have an impact on the drone collision. As the meteorological conditions deteriorate, the probability of drone collision increases, and the safety risk level of the drone will be greatly affected. When analyzing the drone collision risk, it is mainly divided into ground building risk and airspace flight obstacle risk. The height and density of the building and the density and parameters of other aircraft in the airspace will directly affect the drone flight situation. If the drone cannot avoid obstacles effectively, a collision will occur, thereby causing a safety risk accident. In the drone safety risk calculation model on the ground and in the airspace of the region, each indicator is reasonably reflected, and the corresponding safety risk expression is obtained according to the logical relationship and correlation between the indicators. The two types of environmental factors jointly affect the safety risk level of drones. Therefore, the collision risks brought by ground buildings and aerial flight obstacles are accumulated to evaluate the regional drone safety risk. At the same time, the direct impact of meteorological conditions on the flight status of drones is comprehensively considered, thus forming a complete idea for quantitative calculation of regional drone safety risks considering environmental impacts.

[0097] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A regional drone safety risk assessment method, characterized in that: include: Obtaining specific parameters of the drone and preset mission scenario information data, wherein the specific parameters of the drone include body length, wingspan width and body height, and the preset mission scenario information data includes area ratio data of tall buildings in the area, drone density data in the area, specific parameters of aircraft in the area, and data on the number of aircraft in the area; Calculate the regional ground safety risk value and airspace safety risk value based on the specific parameters of the drone and the preset mission scenario information data; Determining meteorological risk assessment indicators, wherein the meteorological risk assessment indicators include visibility, rainfall and wind speed; Weighting each of the meteorological risk assessment indicators is performed based on the analytic hierarchy process, and a meteorological risk assessment coefficient is calculated based on each of the meteorological risk assessment indicators and the corresponding weights; A regional UAV safety risk assessment model is constructed based on the meteorological risk assessment coefficient, the ground safety risk value and the airspace safety risk value, and a safety risk assessment of UAVs in the region is performed based on the regional UAV safety risk assessment model.

2. A regional drone safety risk assessment method according to claim 1, characterized in that: The calculation process of the regional ground safety risk value specifically includes: The contact area and relative speed between the drone and the building are calculated based on the specific parameters of the drone and the area ratio data of tall buildings in the area; and the ground safety risk value is calculated based on the contact area, relative speed and drone density data in the area.

3. A regional drone safety risk assessment method according to claim 2, characterized in that: The ground safety risk value is calculated based on the contact area, relative speed and drone density data in the area, and the specific calculation formula is: A=b1h1 cosα R G =n B ·ρAv1 Where A is the contact area, α is the angle between the plane formed by the wingspan and height directions of the drone and the contact plane, b1 and h1 are the wingspan and height of the drone, v1 is the relative speed of the drone to the building in the vertical direction of the section, and R is G represents the security risk brought by ground buildings in the area per unit time, ρ is the density of drones in the area, and n B This is the area ratio of tall buildings in the region.

4. A regional drone safety risk assessment method according to claim 1, characterized in that: The calculation process of the airspace safety risk value specifically includes: The relative speed between the drone and the aircraft is calculated based on the specific parameters of the drone and the parameter data of other aircraft in the area; the airspace safety risk value is calculated based on the relative speed and the drone density and aircraft quantity data in the area.

5. A regional drone safety risk assessment method according to claim 4, characterized in that: The ground safety risk assessment coefficient is calculated based on the relative speed and the drone density data in the area, and the specific calculation formula is: V=(a+v2t)bh Where V is the volume swept in time t, v2 is the relative speed between the UAV and other aircraft in the airspace, a, b, h are the particle distances between the UAV and the aircraft in the direction of aircraft movement, wingspan direction and vertical direction respectively; R A is the safety risk of drones in the airspace of the region, N is the number of such aircraft in the region, and ρ is the density of drones in the region.

6. A regional drone safety risk assessment method according to claim 1, characterized in that: The calculation process of the meteorological risk assessment coefficient specifically includes: Determine meteorological risk assessment indicators, determine the grade assessment factors corresponding to each meteorological risk assessment indicator based on the severity of the weather, weight each meteorological risk assessment indicator based on the hierarchical analysis method, and calculate the meteorological risk assessment coefficient based on the weights and grade assessment factors corresponding to each meteorological risk assessment indicator.

7. A regional drone safety risk assessment method according to claim 6, characterized in that: The meteorological risk assessment coefficient is calculated based on the weights and grade assessment factors corresponding to each of the meteorological risk assessment indicators. The specific calculation formula is: η=w1×η F +w2×η R +w3×η W Where η is the meteorological risk condition coefficient of the UAV, η F , η R , η w are the evaluation factors corresponding to fog, rainfall and wind phenomena respectively, and w1, w2 and w3 are the weights corresponding to fog, rainfall and wind phenomena respectively.

8. A regional drone safety risk assessment method according to claim 1, characterized in that: The regional drone safety risk assessment model is specifically: In the formula, R U is the safety risk of UAV, η is the meteorological risk assessment coefficient of UAV, R G Represents the safety risk brought by the ground buildings in the area, R A It is a risk to airspace security in the region.

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